ENCAPSULATION MACHINE

With the change and deterioration of the global climate and environment, more and more people are concerned about the current human condition, and constantly proposing various sustainable response strategies to solve existing problems and future situations, to seek a balanced and optimized solution strategy. From the point of view of artificial intelligence, we attempt to explore experiments in which artificial intelligence imitates, learns, optimizes, and rapidly generates solutions. Through testing and predicting the climatic environment of the site and learning from high-quality examples, we are able to set different environmental and climatic conditions and needs and quickly generate sustainable solutions for different geographical environments, site characteristics, and climatic types. We seek a relationship between architecture and landscape, as well as future extensible states. 

During the cloning process, whether the Dolly sheep of architectural design could preserve human intelligence? Could we integrate a cloning and adaptive framework to migrate the order of human decision-making to meet new design solutions and new contexts, where there is decision-making, there is the hustle and bustle, and how could we avoid it? Who is more suitable for cloning the decision-making part of human intelligence, machine learning, or probabilistic mathematical methods, we try to explore the deep mathematical and probabilistic logic behind the form, and for machine learning, what theoretical foundations we need to generate valid parts. Through the massive and rapid self-learning, solution optimization, and complex calculations of artificial intelligence, it is hoped that more effective outcome prediction and faster and more rational sustainable solutions will be made in urban new and renovation environments.